Supervised talking head forgery detection faces severe generalization challenges due to the continuous evolution of generators. By reducing reliance on generator-specific forgery patterns, self-supervised detectors offer stronger cross-generator robustness. However, existing research has mainly focused on building stronger detectors, while the discriminative capacity of trained detectors remains insufficiently exploited. In particular, for score-based self-supervised detectors, the limited discriminative ability on hard cases is often reflected in unreliable anomaly ordering, leaving room for further refinement. Motivated by this observation, we draw inspiration from the dual-system theory of human cognition and propose a Training-Free Dual-System (TFDS) framework to further exploit the latent discriminative capacity of existing score-based self-supervised detectors. TFDS treats anomaly-like scores as the basis of System-1, using lightweight threshold-based routing to partition samples into confident and uncertain subsets. System-2 then revisits only the uncertain subset, performing fine-grained evidence-guided reasoning to refine the relative ordering of ambiguous samples within the original score distribution. Extensive experiments demonstrate consistent improvements across datasets and perturbation settings, with the gains arising mainly from corrected ordering within the uncertain subset. These findings show that existing self-supervised talking head forgery detectors still contain underexploited discriminative cues that can be effectively unlocked through training-free dual-system reasoning.

翻译：有监督的说话人头部伪造检测因生成器的持续进化而面临严重的泛化挑战。通过降低对生成器特定伪造模式的依赖，自监督检测器展现出更强的跨生成器鲁棒性。然而，现有研究主要集中于构建更强的检测器，但已训练检测器的判别能力尚未被充分挖掘。特别地，对于基于分数的自监督检测器，其在困难样本上有限的判别能力常体现为不可靠的异常排序，这为进一步优化留下了空间。受此观察启发，我们从人类认知双系统理论中汲取灵感，提出了一种免训练双系统框架，以进一步挖掘现有基于分数的自监督检测器的潜在判别能力。TFDS将类异常得分视为系统1的基础，利用轻量级阈值路由将样本划分为置信子集和不确定子集。系统2则仅重新处理不确定子集，通过细粒度的证据引导推理，优化原始得分分布中模糊样本的相对排序。大量实验表明，该方法在多个数据集和扰动设置下均能取得一致提升，且性能增益主要源于不确定子集中排序的修正。这些发现表明，现有自监督说话人头部伪造检测器仍含有未被充分挖掘的判别线索，而通过免训练双系统推理可有效解锁这些潜力。